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import gradio as gr
import numpy as np
import torch
import librosa
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
# Constants
SAMPLING_RATE = 16000
MODEL_NAME = "MIT/ast-finetuned-audioset-10-10-0.4593"
DEFAULT_THRESHOLD = 0.7
# Load model and feature extractor
feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_NAME)
model = AutoModelForAudioClassification.from_pretrained(MODEL_NAME)
def analyze_audio(audio_array, threshold=DEFAULT_THRESHOLD):
"""
Process audio and detect anomalies
Returns:
- classification result
- confidence score
- spectrogram visualization
"""
try:
# Handle different audio input formats
if isinstance(audio_array, tuple):
sr, audio = audio_array
if sr != SAMPLING_RATE:
audio = librosa.resample(audio, orig_sr=sr, target_sr=SAMPLING_RATE)
else:
audio = audio_array
if len(audio.shape) > 1:
audio = librosa.to_mono(audio)
# Extract features
inputs = feature_extractor(
audio,
sampling_rate=SAMPLING_RATE,
return_tensors="pt",
padding=True,
return_attention_mask=True
)
# Run inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.softmax(logits, dim=-1)
# Get predicted class and confidence
predicted_class = "Normal" if probs[0][0] > threshold else "Anomaly"
confidence = probs[0][0].item() if predicted_class == "Normal" else 1 - probs[0][0].item()
# Create spectrogram visualization
spectrogram = librosa.feature.melspectrogram(
y=audio,
sr=SAMPLING_RATE,
n_mels=64, # Reduced from 128 to avoid warning
fmax=8000
)
db_spec = librosa.power_to_db(spectrogram, ref=np.max)
fig, ax = plt.subplots(figsize=(10, 4))
img = librosa.display.specshow(
db_spec,
x_axis='time',
y_axis='mel',
sr=SAMPLING_RATE,
fmax=8000,
ax=ax
)
fig.colorbar(img, ax=ax, format='%+2.0f dB')
ax.set(title='Mel Spectrogram')
plt.tight_layout()
plt.savefig('spec.png', bbox_inches='tight')
plt.close()
return (
predicted_class,
f"{confidence:.1%}",
'spec.png',
str(probs.tolist()[0])
)
except Exception as e:
return f"Error: {str(e)}", "", "", ""
# Gradio interface
with gr.Blocks(title="Industrial Audio Analyzer", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# 🏭 Industrial Equipment Sound Analyzer
### Powered by Audio Spectrogram Transformer (AST)
""")
with gr.Row():
with gr.Column():
audio_input = gr.Audio(
label="Upload Equipment Audio Recording",
type="numpy"
)
threshold = gr.Slider(
minimum=0.5,
maximum=0.95,
step=0.05,
value=DEFAULT_THRESHOLD,
label="Anomaly Detection Threshold",
info="Higher values reduce false positives but may miss subtle anomalies"
)
analyze_btn = gr.Button("🔍 Analyze Sound", variant="primary")
with gr.Column():
result_label = gr.Label(label="Detection Result")
confidence = gr.Textbox(label="Confidence Score")
spectrogram = gr.Image(label="Spectrogram Visualization")
raw_probs = gr.Textbox(
label="Model Output Probabilities",
visible=False
)
analyze_btn.click(
fn=analyze_audio,
inputs=[audio_input, threshold],
outputs=[result_label, confidence, spectrogram, raw_probs]
)
gr.Markdown("""
## How It Works
- Upload audio recordings from industrial equipment
- The AI analyzes sound patterns using spectrogram analysis
- Detects anomalies indicating potential equipment issues
**Tip**: For best results, use 5-10 second recordings of steady operation
""")
if __name__ == "__main__":
demo.launch()
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